A Vehicle Color Classification Method for Video Surveillance System Concerning Model-Based Background Subtraction
The vehicle color classification (VCC) becomes a challenge problem in the video surveillance system since the foreground mask derived by the model-based background subtraction algorithm will mislead the VCC by the impure background and the improper foreground regions. In this paper, a novel VCC algorithm based on refining the foreground mask with following two steps is presented to ensure classification result. First, by combining the results of model-based foreground mask and image segmentation, we translate an image into several regions based on the newly developed mask-based connected component labeling algorithm. Secondly, the refined foreground mask is derived by analyzing the region property to remove the undesired regions. The support vector machine (SVM) based classifier with two-layer structure is adopted in this paper. The first layer classifies the image into color and grayscale classes, and the second layer contains two SVM classifiers for the two classes, respectively, which further determines the probability of 7 colors (black, gray, white, red, yellow, green and blue) of the object. It found the average correct rates are respectively 30.2% and 72.3% for the original and refined foreground masks that shows the proposed approach is promising to automatic determination of vehicle colors in a video surveillance system.
Keywordsvehicle color classification foreground refinement background subtraction object detection surveillance system
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